Desiree_Rywal
zagor
2025-11-18
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 617808 33.0 1414808 75.6 686457 36.7
## Vcells 1163527 8.9 8388608 64.0 1877160 14.43 f-ctions
3.1 summary stat
summary_stats <- function(df, measurevar, groupvars, conf.level = 0.95) {
df %>%
dplyr::group_by(across(all_of(groupvars))) %>%
dplyr::summarise(
N = sum(!is.na(.data[[measurevar]])),
mean = mean(.data[[measurevar]], na.rm = TRUE),
sd = sd(.data[[measurevar]], na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(
se = sd / sqrt(N),
ci = se * qt(conf.level/2 + 0.5, N - 1)
)
}3.2 scale to the average of the reference group
process_data <- function(df, groupvars, measurevar, scale_treatment = "noninoculated") {
df.long = df %>%
tidyr::pivot_longer(cols = 3:ncol(df), names_to = "transcript",
values_to = measurevar, values_drop_na = TRUE)
data.SE = summary_stats(df.long, measurevar, groupvars)
scale_reference = data.SE %>%
dplyr::filter(Treatment == scale_treatment) %>%
dplyr::select(Tissue, transcript, mean) %>%
dplyr::rename(scale_mean = mean)
df.scaled <- dplyr::left_join(df.long, scale_reference, by = c("Tissue", "transcript")) %>%
dplyr::mutate(scaled = .data[[measurevar]] / scale_mean) %>%
dplyr::select(-scale_mean)
df.scaled = df.scaled %>%
dplyr::arrange(dplyr::desc(transcript), dplyr::desc(Treatment))
shoots = df.scaled %>% dplyr::filter(Tissue == "shoots")
roots = df.scaled %>% dplyr::filter(Tissue == "roots")
return(list(
shoots = shoots,
roots = roots
))
}3.3 permutation t-test
- for non-i.i.d, hypotheses
- a non-parametric robust alternative to traditional t-tests
- small sample sizes
- robust to unequal variances
perm_test_by_transcript <- function(mydata.long, measurevar = "measurement", groupvar = "Treatment") {
set.seed(123456)
temp = data.frame()
transcript_levels = levels(mydata.long$transcript)
treatment_levels = levels(mydata.long[[groupvar]])
treatment_pairs = combn(treatment_levels, 2, simplify = FALSE)
for(i in transcript_levels) {
data = mydata.long[mydata.long$transcript == i, ]
k = 12 # nrow(data)
pvalues = purrr::map_dbl(treatment_pairs, function(x) {
subset_data = base::subset(data, data[[groupvar]] %in% x)
res = MKinfer::perm.t.test(
formula = stats::as.formula(base::paste(measurevar, "~", groupvar)),
data = subset_data,
alternative = "two.sided",
mu = 0,
paired = FALSE,
var.equal = FALSE,
conf.level = 0.95,
perm.conf.int = 0.95,
symmetric = TRUE,
p.adjust.method = "BH",
detailed = TRUE,
# split k observations into two nontrivial groups
R = sum(choose(k, 1:(k-1))), # alternative (k, k/2)
set.seed = 123456
)
res$perm.p.value
})
tmp = base::as.data.frame(base::t(pvalues))
colnames(tmp) = purrr::map_chr(treatment_pairs, ~base::paste(.x, collapse = ' vs '))
rownames(tmp) = i
temp <- base::rbind(temp, tmp)
}
return(temp)
}3.4 plot
plot_gene_expression <- function(data.SE
, data.long
, stat.test.sig
, transcripts_excl = c('13-LOX', 'PTI5')
, color_values
, facet_cols = 6
, y_scales = NULL
, dodge_width = 0.8,
plot_title = ""
) {
dodge = position_dodge(width = dodge_width)
data.SE.filtered = dplyr::filter(data.SE, !transcript %in% transcripts_excl)
data.long.filtered = dplyr::filter(data.long, !transcript %in% transcripts_excl)
stat.test.filtered = NULL
if (nrow(stat.test.sig) > 0) stat.test.filtered = dplyr::filter(stat.test.sig, !transcript %in% transcripts_excl)
p = ggplot(data.SE.filtered, aes(x = Treatment, y = mean)) +
geom_point(size=3.5, shape = 22, position = dodge, aes(fill = Treatment), colour = "black") +
geom_point(data = data.long.filtered, aes(x = Treatment, y = scaled, fill = Treatment), colour = "black",
size = 2.0, shape = 21,
position = dodge) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.3, lwd = 0.5,
position = dodge) +
facet_wrap(~ transcript, ncol = facet_cols, scales = "free", drop = TRUE)
if (!is.null(y_scales)) {
p = p + ggh4x::facetted_pos_scales(y = y_scales)
}
p = p +
# geom_hline(yintercept = 2.5, alpha = 0.0) +
geom_hline(yintercept = 0, alpha = 0.0) +
geom_hline(yintercept = 1, alpha = 0.5, linetype = "dotted", col = "gray45") +
ggtitle(plot_title) +
theme_bw() +
scale_colour_manual(name = "", values = rev(color_values)) +
scale_fill_manual(name = "", values = rev(color_values)) +
labs(x = "", y = "Relative gene expression (+/- SE)"
# , subtitle = "Permutation t-test measurements"
) +
theme(plot.subtitle = element_text(size = 10),
axis.text = element_text(size = 12.5),
axis.text.x = element_text(size = 12.5, angle = 90),
axis.title = element_text(size = 12.5, face = "bold"),
strip.text = element_text(size = 12.5),
title = element_text(size = 15, face = "bold"),
# axis.ticks.x = element_blank(),
legend.key.height = unit(1.5, "cm"),
legend.key.width = unit(1.75, "cm"),
legend.text = element_text(size = 12.5),
legend.title = element_text(size = 12.5),
legend.background = element_rect(fill = "transparent", size = 0.5, linetype = "dotted"),
legend.position = "top",
legend.justification = "right",
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y.right = element_blank(),
axis.text.y.right = element_blank(),
# axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 12.5, margin = margin(r = 0)),
panel.spacing = unit(1, "lines"),
strip.background = element_rect(size = 0.5, fill = "transparent", color = NA) ,
panel.border = element_blank(),
axis.line = element_line(color = "black")
)
p = p + theme(legend.position = "none")
if (!is.null(stat.test.filtered) && nrow(stat.test.filtered) > 0) {
p = p + ggpubr::stat_pvalue_manual(
stat.test.filtered,
label = "p.adj.signif",
xmin = "xmin",
xmax = "xmax",
y.position = "y.position",
hide.ns = FALSE,
tip.length = 0.01,
step.increase = 0.0,
inherit.aes = FALSE
)
}
return(p)
}3.5 distribution, variance and effects
- on qPCR data
- quantGenius - quantification of qPCR data using standard curve
- MIQE 2.0: Revision of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines
- quantification based on a standard curve inherently involves a log transformation of the input data
- Denote: LOQ values skew distributions
- Denote: distribution tests: low power for small sample size, see QQ of residuals
- if most or all points fall inside the shaded confidence band, the sample’s distribution does not show strong evidence of departure from normality at the plotted sample size and confidence level
- a few isolated points outside the band at the extremes are common with small samples and do not necessarily indicate a severe problem
- Denote: heteroscedasticity: some tests are meant to be used with normally distributed data, but can tolerate relatively low deviation from normality; Levene’s test with mean, more robust test Brown-Forsythe with median, Fligner-Killeen when data are non-normally distributed or when problems related to outliers in the dataset cannot be resolved
- Denote: effect size: Cohen’s d - parametric with assumptions, Wilcoxon Effect Size - the non-parametric alternative; different sensitivity to outliers
dens_and_effect <- function(mydata.long, p_colors) {
# Plot density with x="measurement"
p1 = ggpubr::ggdensity(mydata.long, x = "measurement",
add = "mean", rug = TRUE,
color = "Treatment", fill = "Treatment",
facet.by = 'transcript') +
scale_fill_manual(name = "", values = rev(p_colors)) +
scale_color_manual(name = "", values = rev(p_colors)) +
ggtitle("measurement") +
facet_wrap(~transcript, ncol = 4, scales = "free")
print(p1)
# optional: Plot density with x="scaled"- sam e shape wih a shift
# p2 = ggpubr::ggdensity(mydata.long, x = "scaled",
# add = "mean", rug = TRUE,
# color = "Treatment", fill = "Treatment",
# facet.by = 'transcript') +
# scale_fill_manual(name = "", values = rev(p_colors)) +
# scale_color_manual(name = "", values = rev(p_colors)) +
# ggtitle("scaled") +
# facet_wrap(~transcript, ncol = 4, scales = "free")
# print(p2)
cat(crayon::red('#### #### \nDistribution tests\n#### #### \n'))
distr_tests = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_map(~ {
x = na.omit(.x$measurement)
if (length(x) < 3) {
return(tibble::tibble(
Shapiro_Wilk = NA_real_,
Anderson_Darling = NA_real_,
Lilliefors_KS = NA_real_,
Jarque_Bera = NA_real_,
DAgostino_Skewness = NA_real_,
n = length(x),
transcript = .y$transcript
))
}
tibble::tibble(
Shapiro_Wilk = tryCatch(shapiro.test(x)$p.value, error = function(e) NA_real_),
Anderson_Darling = tryCatch(nortest::ad.test(x)$p.value, error = function(e) NA_real_),
Lilliefors_KS = tryCatch(nortest::lillie.test(x)$p.value, error = function(e) NA_real_),
Jarque_Bera = tryCatch(tseries::jarque.bera.test(x)$p.value, error = function(e) NA_real_),
DAgostino_Skewness = tryCatch(moments::agostino.test(x)$p.value, error = function(e) NA_real_),
n = length(x),
transcript = .y$transcript
)
}) %>%
dplyr::bind_rows() %>%
dplyr::ungroup() %>%
dplyr::mutate(
Shapiro_Wilk_BH = stats::p.adjust(Shapiro_Wilk, method = "BH"),
Anderson_Darling_BH = stats::p.adjust(Anderson_Darling, method = "BH"),
Lilliefors_KS_BH = stats::p.adjust(Lilliefors_KS, method = "BH"),
Jarque_Bera_BH = stats::p.adjust(Jarque_Bera, method = "BH"),
DAgostino_Skewness_BH = stats::p.adjust(DAgostino_Skewness, method = "BH")
)
combined_results = distr_tests[grep("^n$|transcript|_BH", colnames(distr_tests))]
print(combined_results)
cat(crayon::red("#### #### \nQuantile-Quantile plots\n#### #### \n"))
qq_plot_list = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_map(~ {
ggpubr::ggqqplot(.x$measurement) +
ggplot2::ggtitle(paste("Raw:", .y$transcript))
})
resid_qq_list = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_map(~ {
model = lm(measurement ~ Treatment, data = .x)
resids = resid(model)
ggpubr::ggqqplot(resids) +
ggplot2::ggtitle(paste("Residuals:", .y$transcript))
})
resid_density_list = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_map(~ {
model = lm(measurement ~ Treatment, data = .x)
resids = scale(resid(model)) # standardize residuals
df = data.frame(resids = as.numeric(resids))
ggplot(df, aes(x = resids)) +
geom_density(fill = "steelblue", alpha = 0.6) +
stat_function(fun = dnorm, color = "red", linetype = "dashed") +
ggtitle(paste("Residuals:", .y$transcript)) +
theme_minimal()
})
# Arrange and print all plots
qq_arranged = ggpubr::ggarrange(plotlist = qq_plot_list, ncol = 4, nrow = ceiling(length(qq_plot_list) / 4))
resid_qq_arranged = ggpubr::ggarrange(plotlist = resid_qq_list, ncol = 4, nrow = ceiling(length(resid_qq_list) / 4))
resid_hist_arranged = ggpubr::ggarrange(plotlist = resid_density_list, ncol = 4, nrow = ceiling(length(resid_density_list) / 4))
print(qq_arranged)
print(resid_qq_arranged)
print(resid_hist_arranged)
# Return invisibly
invisible(list(qq = qq_arranged, resid_qq = resid_qq_arranged, resid_hist = resid_hist_arranged))
cat(crayon::red("#### #### \nTest for homogeneity of variance across groups\n#### #### \n"))
cat(crayon::red("#### #### \nLevene\n#### #### \n"))
# Levene's test on measurement
lev_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::levene_test(measurement ~ Treatment, center = "mean")
print(lev_meas)
cat(crayon::red("#### #### \nBrown-Forsythe\n#### #### \n"))
# center = "median" switches Levene’s test to the Brown-Forsythe variant
bf_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::levene_test(measurement ~ Treatment, center = "median")
print(bf_meas)
cat(crayon::red("#### #### \nFligner\n#### #### \n"))
fk_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_modify(~ broom::tidy(stats::fligner.test(measurement ~ Treatment, data = .x)))
print(fk_meas)
cat(crayon::red("#### #### \nWilcoxon effect size\n#### #### \n"))
# Wilcoxon effect size on measurement
eff_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::wilcox_effsize(measurement ~ Treatment)
print(eff_meas)
# Wilcoxon effect size on scaled
# eff_scaled = mydata.long %>%
# dplyr::group_by(transcript) %>%
# rstatix::wilcox_effsize(scaled ~ Treatment)
# print(eff_scaled)
cat(crayon::red("#### #### \nCohen's d Measure of Effect Size\n#### #### \n"))
# Cohen's d on measurement
coh_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::cohens_d(measurement ~ Treatment, paired = FALSE)
print(coh_meas)
invisible(list(
p_measurement = p1,
distr_measurement = combined_results,
levene_measurement = lev_meas,
brownforsythe_measurement = bf_meas,
fligner_measurement = fk_meas,
wilcoxon_measurement = eff_meas,
cohensd_measurement = coh_meas
))
}3.6 ggplot limits
make_scale_params <- function(maxval) {
upper = ifelse(maxval > 2, maxval + 2, maxval + 0.1) # 0.5
step = dplyr::case_when(
maxval >= 50 ~ 25,
maxval >= 30 ~ 10,
maxval >= 15 ~ 5,
maxval >= 10 ~ 2,
maxval > 2 ~ 1,
TRUE ~ 0.5
)
upper_round = ceiling(upper / step) * step
breaks = seq(0, upper_round, by = step)
list(limits = c(0, upper_round), breaks = breaks)
}
build_y_scales_for <- function(names_vec, max_per_transcript) {
max_per_transcript %>%
dplyr::filter(transcript %in% names_vec) %>%
purrr::pmap(function(transcript, max_scaled) {
params = make_scale_params(max_scaled)
lhs = rlang::expr(transcript == !!transcript)
rhs = rlang::expr(ggplot2::scale_y_continuous(limits = !!params$limits, breaks = !!params$breaks))
rlang::new_formula(lhs, rhs)
})
}3.7 test and plot
test_and_plot <- function(data_long_raw
, myorder
, pal
, what
, plot_gene_expression_func
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
) {
mydata.long = dplyr::as_tibble(data.table::data.table(data_long_raw))
mydata.long$transcript = factor(mydata.long$transcript, levels = myorder)
mydata.long = mydata.long %>% dplyr::arrange(factor(transcript, levels = myorder))
mydata.long.SE = summary_stats(mydata.long, measurevar = "scaled", groupvars = groupvars)
results = dens_and_effect(mydata.long, p_colors = pal)
cat(what, file = fr, append = TRUE, sep = "\n")
cat("\nDistribution tests", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$distr_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$distr_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("\nLevene's test for homogeneity of variance across groups", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$levene_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$levene_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("\nBrown-Forsythe (Levene with median)", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$brownforsythe_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$brownforsythe_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("\nFligner-Killeen test of homogeneity of variances", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$fligner_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$fligner_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("\nWilcoxon effect size", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$wilcoxon_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$wilcoxon_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("\nCohen's d Measure of Effect Size", file = fr, append = TRUE, sep = "\n")
header = paste(colnames(results$cohensd_measurement), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(results$cohensd_measurement, 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
temp = perm_test_by_transcript(mydata.long, measurevar = "measurement", groupvar = "Treatment")
temp$transcript = rownames(temp)
perm = tidyr::gather(temp, contrast, perm.p, colnames(temp)[1]:colnames(temp)[ncol(temp)-1], factor_key=TRUE)
perm$group1 = gsub(' vs.*', '', perm$contrast)
perm$group2 = gsub('.* vs ', '', perm$contrast)
perm$perm.p.adj = p.adjust(perm$perm.p, method = 'BH')
perm$perm.p.adj.signif = 'ns'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.0001] = '**'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.001] = '**'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.05] = '*'
stat.test = perm %>%
dplyr::select(transcript, group1, group2, perm.p, perm.p.adj, perm.p.adj.signif) %>%
dplyr::rename(
p = perm.p,
p.adj = perm.p.adj,
p.adj.signif = perm.p.adj.signif
)
# Compute y-position using both mean ± se and scaled values
y_pos_df = mydata.long.SE %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(
max_mean_se = max(mean + se, na.rm = TRUE),
min_mean_se = min(mean - se, na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::left_join(
mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(
max_scaled = max(scaled, na.rm = TRUE),
min_scaled = min(scaled, na.rm = TRUE),
.groups = "drop"
),
by = "transcript"
) %>%
dplyr::mutate(
plot_max = pmax(max_mean_se, max_scaled, na.rm = TRUE),
plot_min = pmin(min_mean_se, min_scaled, na.rm = TRUE),
plot_range = pmax(plot_max - plot_min, 1e-6)
) %>%
dplyr::select(transcript, plot_max, plot_range)
# Map group names to numeric x positions
pos_map = mydata.long %>%
dplyr::distinct(transcript, Treatment) %>%
dplyr::mutate(xpos = as.numeric(factor(Treatment, levels = levels(mydata.long$Treatment))))
# Attach y-position and x-position info to stat.test
stat.test = stat.test %>%
dplyr::left_join(y_pos_df, by = "transcript") %>%
dplyr::left_join(pos_map %>% dplyr::rename(group1 = Treatment, xmin = xpos), by = c("transcript", "group1")) %>%
dplyr::left_join(pos_map %>% dplyr::rename(group2 = Treatment, xmax = xpos), by = c("transcript", "group2")) %>%
dplyr::mutate(
xmin = dplyr::if_else(is.na(xmin), as.numeric(factor(group1, levels = levels(mydata.long$Treatment))), xmin),
xmax = dplyr::if_else(is.na(xmax), as.numeric(factor(group2, levels = levels(mydata.long$Treatment))), xmax)
)
# Compute distinct y.position per comparison for significant results
stat.test.sig = stat.test %>%
dplyr::filter(!is.na(p.adj) & p.adj <= 0.05) %>%
dplyr::group_by(transcript) %>%
dplyr::arrange(xmin, xmax, .by_group = TRUE) %>%
dplyr::mutate(
base_y = plot_max + 0.03 * plot_range,
inc = 0.05 * plot_range,
y.position = base_y + (dplyr::row_number() - 1) * inc
) %>%
dplyr::ungroup() %>%
dplyr::select(transcript, group1, group2, p.adj, p.adj.signif, y.position, xmin, xmax)
group1 = sapply(y_scales1, function(x) {
s = deparse(x)
s_full = paste(s, collapse = " ")
sub('.*transcript == *"([^"]+)".*', '\\1', s_full)
})
group2 = sapply(y_scales2, function(x) {
s = deparse(x)
s_full = paste(s, collapse = " ")
sub('.*transcript == *"([^"]+)".*', '\\1', s_full)
})
p1 = plot_gene_expression_func(
data.SE = mydata.long.SE,
data.long = mydata.long,
stat.test.sig = stat.test.sig,
transcripts_excl = group2,
facet_cols = 2,
color_values = pal,
plot_title = what,
y_scales = y_scales1,
dodge_width = 0.8
)
print(p1)
p2 = plot_gene_expression_func(
data.SE = mydata.long.SE,
data.long = mydata.long,
stat.test.sig = stat.test.sig,
transcripts_excl = group1,
facet_cols = 6,
color_values = pal,
plot_title = what,
y_scales = y_scales2,
dodge_width = 0.8
)
print(p2)
return(list(plot1 = p1, plot2 = p2, stat.test = stat.test))
}4 Desiree
4.1 input
## [1] "~$Table_combinedInputs.xlsx" "README.md"
## [3] "Table_combinedInputs.xlsx"fn = 'Table_combinedInputs.xlsx'
wb = openxlsx::loadWorkbook(file.path(fp, fn))
sheet_names = names(wb)
sheet_name = grep("S5_Desiree_Rywal", sheet_names, value = TRUE)
myTable = openxlsx::read.xlsx(xlsxFile = file.path(fp, fn),
sheet = sheet_name,
startRow = 10,
colNames = TRUE,
rowNames = FALSE,
detectDates = FALSE,
skipEmptyRows = TRUE,
skipEmptyCols = TRUE,
rows = NULL,
cols = NULL,
check.names = FALSE,
sep.names = ".",
namedRegion = NULL,
na.strings = "NA",
fillMergedCells = FALSE)
myTable$Genotype = trimws(myTable$Genotype)
Desiree = myTable[myTable$Genotype == 'Desiree', ]
data.table::setDT(Desiree)
Desiree[, SampleID := NULL]
Desiree[, Genotype := NULL]
Desiree$Tissue = as.factor(trimws(Desiree$Tissue))
Desiree$Treatment = factor(trimws(Desiree$Treatment), levels = c("noninoculated", "inoculated"))4.2 params
my_dir = file.path("..", "reports", "Desiree_Rywal")
if (!dir.exists(my_dir)) {
dir.create(my_dir)
}
fr = file.path('..', 'output', 'Desiree_Rywal_stat.txt')
file.create(fr)## [1] TRUE4.3 results
cat("Desiree", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
run_analysis_for_strain <- function(data
, strain
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot) {
strains = trimws(unique(na.omit(data$Strain)))
temp = data[grep(setdiff(strains, strain), data$Strain, invert = TRUE), ]
temp[, Strain := NULL]
shoots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$shoots
roots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$roots
max_per_transcript = shoots %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(max_scaled = max(scaled, na.rm = TRUE), .groups = "drop")
group2_names = setdiff(max_per_transcript$transcript, group1_names)
y_scales1 = build_y_scales_for(group1_names, max_per_transcript)
y_scales2 = build_y_scales_for(group2_names, max_per_transcript)
max_per_transcript = roots %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(max_scaled = max(scaled, na.rm = TRUE), .groups = "drop")
y_scales3 = build_y_scales_for(group1_names, max_per_transcript)
y_scales4 = build_y_scales_for(group2_names, max_per_transcript)
result_shoots = test_and_plot(data_long_raw = shoots,
myorder = myorder,
pal = pal,
what = paste("shoots", strain),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales1,
y_scales2 = y_scales2)
res = result_shoots$stat.test[, grep("transcript|group1|group2|^p$|p\\.", colnames(result_shoots$stat.test))]
print(res[res$p.adj.signif != 'ns', ])
cat("", file = fr, append = TRUE, sep = "\n")
output_text = "permutational t-test"
cat(output_text, file = fr, append = TRUE, sep = "\n")
header = paste(colnames(res), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(as.data.frame(tibble::as_tibble(res)), 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Desiree_shoots.", gsub(" ", "", strain), "_1.pdf")),
plot = result_shoots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_shoots.", gsub(" ", "", strain), "_1.svg")),
plot = result_shoots$plot1, device = svg, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_shoots.", gsub(" ", "", strain), "_2.pdf")),
plot = result_shoots$plot2, device = pdf, width = 9, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_shoots.", gsub(" ", "", strain), "_2.svg")),
plot = result_shoots$plot2, device = svg, width = 9, height = 8, units = "in", dpi = 900)
result_roots = test_and_plot(data_long_raw = roots,
myorder = myorder,
pal = pal,
what = paste("roots", strain),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales3,
y_scales2 = y_scales4)
res = result_roots$stat.test[, grep("transcript|group1|group2|^p$|p\\.", colnames(result_roots$stat.test))]
print(res[res$p.adj.signif != 'ns', ])
cat("", file = fr, append = TRUE, sep = "\n")
output_text = "permutational t-test"
cat(output_text, file = fr, append = TRUE, sep = "\n")
header = paste(colnames(res), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(as.data.frame(tibble::as_tibble(res)), 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Desiree_roots.", gsub(" ", ".", strain), "_1.pdf")),
plot = result_roots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_roots.", gsub(" ", ".", strain), "_1.svg")),
plot = result_roots$plot1, device = svg, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_roots.", gsub(" ", ".", strain), "_2.pdf")),
plot = result_roots$plot2, device = pdf, width = 9, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Desiree_roots.", gsub(" ", ".", strain), "_2.svg")),
plot = result_roots$plot2, device = svg, width = 9, height = 8, units = "in", dpi = 900)
list(shoots = result_shoots, roots = result_roots)
}
results_PS216 = run_analysis_for_strain(data = Desiree
, strain = "PS-216"
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot)
## #### ####
## Distribution tests
## #### ###### # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.0222 0.0554 0.471
## 2 12 StPR1B 0.495 0.585 0.604
## 3 10 StCPI8 0.132 0.139 0.239
## 4 9 StCAB 0.115 0.112 0.246
## 5 11 StBGLU2 0.000110 0.000245 0.00649
## 6 12 StHSP70 0.495 0.585 0.578
## 7 12 StPti5 0.0158 0.0141 0.0298
## 8 12 St13-LOX 0.853 0.796 0.604
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 3.44 0.0935
## 2 StPR1B 1 10 0.925 0.359
## 3 StCPI8 1 8 5.29 0.0505
## 4 StCAB 1 7 1.97 0.203
## 5 StBGLU2 1 9 2.66 0.137
## 6 StHSP70 1 10 10.1 0.00997
## 7 StPti5 1 10 9.19 0.0127
## 8 St13-LOX 1 10 1.98 0.190
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 2.15 0.173
## 2 StPR1B 1 10 0.769 0.401
## 3 StCPI8 1 8 4.30 0.0719
## 4 StCAB 1 7 1.34 0.286
## 5 StBGLU2 1 9 0.639 0.444
## 6 StHSP70 1 10 1.02 0.335
## 7 StPti5 1 10 3.33 0.0981
## 8 St13-LOX 1 10 0.716 0.417
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 4.16 0.0414 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 0.587 0.444 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 2.52 0.113 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 1.31 0.252 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 0.0112 0.916 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 0.00257 0.960 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 5.29 0.0215 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 0.254 0.614 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.832 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.139 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0.826 StCPI8 5 5 large
## 4 measurement noninoculated inoculated 0.816 StCAB 5 4 large
## 5 measurement noninoculated inoculated 0.220 StBGLU2 5 6 small
## 6 measurement noninoculated inoculated 0.277 StHSP70 6 6 small
## 7 measurement noninoculated inoculated 0.786 StPti5 6 6 large
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.81 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated -0.386 StPR1B 6 6 small
## 3 measurement noninoculated inoculated -4.61 StCPI8 5 5 large
## 4 measurement noninoculated inoculated 7.35 StCAB 5 4 large
## 5 measurement noninoculated inoculated -0.595 StBGLU2 5 6 moderate
## 6 measurement noninoculated inoculated -0.215 StHSP70 6 6 small
## 7 measurement noninoculated inoculated -1.49 StPti5 6 6 large
## 8 measurement noninoculated inoculated -2.82 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0002442599 0.0004885198 *
## 3 StCPI8 noninoculated inoculated 0.0002442599 0.0004885198 *
## 4 StCAB noninoculated inoculated 0.0002442599 0.0004885198 *
## 7 StPti5 noninoculated inoculated 0.0036638984 0.0058622374 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.0004885198 *
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.0785 0.0784 0.0433
## 2 12 StPR1B 0.000103 0.00000113 0.00000420
## 3 12 StCPI8 0.558 0.670 0.769
## 4 9 StCAB 0.558 0.670 0.769
## 5 12 StBGLU2 0.0785 0.0830 0.0433
## 6 12 StHSP70 0.809 0.827 0.769
## 7 11 StPti5 0.0102 0.0139 0.00702
## 8 12 St13-LOX 0.657 0.768 0.769
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 5.58 0.0399
## 2 StPR1B 1 10 4.63 0.0569
## 3 StCPI8 1 10 0.246 0.630
## 4 StCAB 1 7 2.28 0.175
## 5 StBGLU2 1 10 4.77 0.0539
## 6 StHSP70 1 10 1.58 0.238
## 7 StPti5 1 9 7.91 0.0203
## 8 St13-LOX 1 10 1.59 0.236
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 4.02 0.0729
## 2 StPR1B 1 10 1.38 0.268
## 3 StCPI8 1 10 0.354 0.565
## 4 StCAB 1 7 2.48 0.159
## 5 StBGLU2 1 10 4.18 0.0682
## 6 StHSP70 1 10 1.30 0.281
## 7 StPti5 1 9 6.44 0.0318
## 8 St13-LOX 1 10 1.45 0.257
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 2.99 0.0840 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 1.14 0.285 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 0.521 0.470 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 2.89 0.0890 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 3.41 0.0649 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 2.27 0.132 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 6.64 0.00998 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 0.784 0.376 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.740 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.233 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0 StCPI8 6 6 small
## 4 measurement noninoculated inoculated 0.602 StCAB 6 3 large
## 5 measurement noninoculated inoculated 0.370 StBGLU2 6 6 moderate
## 6 measurement noninoculated inoculated 0.324 StHSP70 6 6 moderate
## 7 measurement noninoculated inoculated 0.330 StPti5 5 6 moderate
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 2.37 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.647 StPR1B 6 6 moderate
## 3 measurement noninoculated inoculated -0.0549 StCPI8 6 6 negligible
## 4 measurement noninoculated inoculated 1.56 StCAB 6 3 large
## 5 measurement noninoculated inoculated 0.850 StBGLU2 6 6 large
## 6 measurement noninoculated inoculated 0.669 StHSP70 6 6 moderate
## 7 measurement noninoculated inoculated -1.00 StPti5 5 6 large
## 8 measurement noninoculated inoculated -2.64 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0031753786 0.012701514 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.001954079 *
results_PS218 = run_analysis_for_strain(data = Desiree
, strain = "PS-218"
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot)
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.0286 0.0580 0.436
## 2 12 StPR1B 0.0253 0.0358 0.117
## 3 11 StCPI8 0.00614 0.00812 0.0220
## 4 11 StCAB 0.0452 0.0443 0.0458
## 5 11 StBGLU2 0.100 0.101 0.117
## 6 12 StHSP70 0.0253 0.0120 0.0220
## 7 11 StPti5 0.0103 0.00812 0.0220
## 8 12 St13-LOX 0.289 0.209 0.117
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 2.07 0.181
## 2 StPR1B 1 10 0.843 0.380
## 3 StCPI8 1 9 8.34 0.0179
## 4 StCAB 1 9 2.03 0.188
## 5 StBGLU2 1 9 0.484 0.504
## 6 StHSP70 1 10 1.06 0.328
## 7 StPti5 1 9 17.9 0.00219
## 8 St13-LOX 1 10 6.47 0.0292
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 1.32 0.277
## 2 StPR1B 1 10 0.0573 0.816
## 3 StCPI8 1 9 2.22 0.171
## 4 StCAB 1 9 1.43 0.263
## 5 StBGLU2 1 9 0.0630 0.808
## 6 StHSP70 1 10 0.113 0.743
## 7 StPti5 1 9 11.6 0.00771
## 8 St13-LOX 1 10 5.91 0.0354
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 1.41 0.234 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 0.327 0.567 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 2.89 0.0890 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 0.640 0.424 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 0.0111 0.916 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 0.00469 0.945 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 3.36 0.0666 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 5.30 0.0213 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.740 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.139 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0.826 StCPI8 5 6 large
## 4 measurement noninoculated inoculated 0.826 StCAB 5 6 large
## 5 measurement noninoculated inoculated 0.165 StBGLU2 5 6 small
## 6 measurement noninoculated inoculated 0.231 StHSP70 6 6 small
## 7 measurement noninoculated inoculated 0.826 StPti5 6 5 large
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.60 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated -0.0819 StPR1B 6 6 negligible
## 3 measurement noninoculated inoculated -1.44 StCPI8 5 6 large
## 4 measurement noninoculated inoculated 6.54 StCAB 5 6 large
## 5 measurement noninoculated inoculated -0.338 StBGLU2 5 6 small
## 6 measurement noninoculated inoculated -0.0320 StHSP70 6 6 negligible
## 7 measurement noninoculated inoculated -1.97 StPti5 6 5 large
## 8 measurement noninoculated inoculated -2.36 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0017098192 0.0034196385 *
## 3 StCPI8 noninoculated inoculated 0.0058622374 0.0093795799 *
## 4 StCAB noninoculated inoculated 0.0002442599 0.0006513597 *
## 7 StPti5 noninoculated inoculated 0.0002442599 0.0006513597 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.0006513597 *
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.0476 0.0339 0.0170
## 2 12 StPR1B 0.000146 0.00000400 0.0000108
## 3 12 StCPI8 0.0472 0.0339 0.0452
## 4 10 StCAB 0.0605 0.0777 0.113
## 5 11 StBGLU2 0.245 0.306 0.440
## 6 12 StHSP70 0.245 0.377 0.533
## 7 10 StPti5 0.0221 0.0250 0.0233
## 8 12 St13-LOX 0.0127 0.00692 0.00302
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 6.22 0.0318
## 2 StPR1B 1 10 4.05 0.0720
## 3 StCPI8 1 10 1.91 0.197
## 4 StCAB 1 8 9.88 0.0138
## 5 StBGLU2 1 9 11.0 0.00903
## 6 StHSP70 1 10 2.07 0.181
## 7 StPti5 1 8 7.63 0.0246
## 8 St13-LOX 1 10 10.1 0.00975
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 4.38 0.0628
## 2 StPR1B 1 10 1.14 0.312
## 3 StCPI8 1 10 2.30 0.160
## 4 StCAB 1 8 9.79 0.0140
## 5 StBGLU2 1 9 9.75 0.0123
## 6 StHSP70 1 10 1.42 0.261
## 7 StPti5 1 8 5.10 0.0539
## 8 St13-LOX 1 10 9.69 0.0110
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 3.85 0.0498 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 0.633 0.426 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 1.79 0.181 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 5.59 0.0181 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 5.12 0.0236 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 0.982 0.322 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 3.19 0.0739 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 7.59 0.00588 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.832 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.256 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0.555 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 0.809 StCAB 6 4 large
## 5 measurement noninoculated inoculated 0.220 StBGLU2 6 5 small
## 6 measurement noninoculated inoculated 0.601 StHSP70 6 6 large
## 7 measurement noninoculated inoculated 0.826 StPti5 5 5 large
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 2.65 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.702 StPR1B 6 6 moderate
## 3 measurement noninoculated inoculated 1.31 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 2.20 StCAB 6 4 large
## 5 measurement noninoculated inoculated 0.652 StBGLU2 6 5 moderate
## 6 measurement noninoculated inoculated -1.55 StHSP70 6 6 large
## 7 measurement noninoculated inoculated -3.67 StPti5 5 5 large
## 8 measurement noninoculated inoculated -1.88 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0002442599 0.0009770396 *
## 4 StCAB noninoculated inoculated 0.0041524182 0.0083048363 *
## 6 StHSP70 noninoculated inoculated 0.0288226673 0.0461162677 *
## 7 StPti5 noninoculated inoculated 0.0002442599 0.0009770396 *
## 8 St13-LOX noninoculated inoculated 0.0014655594 0.0039081583 *
5 Rywal
5.1 input
5.2 params
5.3 results
cat("Rywal", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
run_analysis_for_strain <- function(data
, strain
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot) {
strains = trimws(unique(na.omit(data$Strain)))
temp = data[grep(setdiff(strains, strain), data$Strain, invert = TRUE), ]
temp[, Strain := NULL]
shoots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$shoots
roots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$roots
max_per_transcript = shoots %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(max_scaled = max(scaled, na.rm = TRUE), .groups = "drop")
group2_names = setdiff(max_per_transcript$transcript, group1_names)
y_scales1 = build_y_scales_for(group1_names, max_per_transcript)
y_scales2 = build_y_scales_for(group2_names, max_per_transcript)
max_per_transcript = roots %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(max_scaled = max(scaled, na.rm = TRUE), .groups = "drop")
y_scales3 = build_y_scales_for(group1_names, max_per_transcript)
y_scales4 = build_y_scales_for(group2_names, max_per_transcript)
result_shoots = test_and_plot(data_long_raw = shoots,
myorder = myorder,
pal = pal,
what = paste("shoots", strain),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales1,
y_scales2 = y_scales2)
res = result_shoots$stat.test[, grep("transcript|group1|group2|^p$|p\\.", colnames(result_shoots$stat.test))]
print(res[res$p.adj.signif != 'ns', ])
cat("", file = fr, append = TRUE, sep = "\n")
output_text = "permutational t-test"
cat(output_text, file = fr, append = TRUE, sep = "\n")
header = paste(colnames(res), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(as.data.frame(tibble::as_tibble(res)), 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Rywal_shoots.", gsub(" ", "", strain), "_1.pdf")),
plot = result_shoots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_shoots.", gsub(" ", "", strain), "_1.svg")),
plot = result_shoots$plot1, device = svg, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_shoots.", gsub(" ", "", strain), "_2.pdf")),
plot = result_shoots$plot2, device = pdf, width = 9, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_shoots.", gsub(" ", "", strain), "_2.svg")),
plot = result_shoots$plot2, device = svg, width = 9, height = 8, units = "in", dpi = 900)
result_roots = test_and_plot(data_long_raw = roots,
myorder = myorder,
pal = pal,
what = paste("roots", strain),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales3,
y_scales2 = y_scales4)
res = result_roots$stat.test[, grep("transcript|group1|group2|^p$|p\\.", colnames(result_roots$stat.test))]
print(res[res$p.adj.signif != 'ns', ])
cat("", file = fr, append = TRUE, sep = "\n")
output_text = "permutational t-test"
cat(output_text, file = fr, append = TRUE, sep = "\n")
header = paste(colnames(res), collapse = "\t")
cat(header, file = fr, append = TRUE, sep = "\n")
output_text = apply(as.data.frame(tibble::as_tibble(res)), 1, function(row) paste(row, collapse = "\t"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Rywal_roots.", gsub(" ", ".", strain), "_1.pdf")),
plot = result_roots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_roots.", gsub(" ", ".", strain), "_1.svg")),
plot = result_roots$plot1, device = svg, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_roots.", gsub(" ", ".", strain), "_2.pdf")),
plot = result_roots$plot2, device = pdf, width = 9, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Rywal_roots.", gsub(" ", ".", strain), "_2.svg")),
plot = result_roots$plot2, device = svg, width = 9, height = 8, units = "in", dpi = 900)
list(shoots = result_shoots, roots = result_roots)
}
results_PS216 = run_analysis_for_strain(data = Rywal
, strain = "PS-216"
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot)
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.299 0.365 0.724
## 2 12 StPR1B 0.107 0.0861 0.154
## 3 12 StCPI8 0.00282 0.000428 0.00514
## 4 10 StCAB 0.107 0.0861 0.154
## 5 12 StBGLU2 0.107 0.0861 0.154
## 6 12 StHSP70 0.921 0.715 0.768
## 7 12 StPti5 0.169 0.268 0.609
## 8 12 St13-LOX 0.107 0.0861 0.154
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 2.11 0.177
## 2 StPR1B 1 10 4.52 0.0595
## 3 StCPI8 1 10 24.6 0.000567
## 4 StCAB 1 8 3.60 0.0944
## 5 StBGLU2 1 10 5.34 0.0435
## 6 StHSP70 1 10 3.15 0.106
## 7 StPti5 1 10 2.91 0.119
## 8 St13-LOX 1 10 4.12 0.0698
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 1.86 0.202
## 2 StPR1B 1 10 3.35 0.0970
## 3 StCPI8 1 10 6.58 0.0281
## 4 StCAB 1 8 3.08 0.117
## 5 StBGLU2 1 10 0.842 0.380
## 6 StHSP70 1 10 3.06 0.111
## 7 StPti5 1 10 0.494 0.498
## 8 St13-LOX 1 10 2.13 0.175
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 1.42 0.234 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 3.41 0.0649 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 7.57 0.00592 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 3.01 0.0826 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 0.254 0.614 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 3.38 0.0660 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 0.00225 0.962 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 3.57 0.0588 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.832 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.0462 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0.832 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 0.809 StCAB 4 6 large
## 5 measurement noninoculated inoculated 0 StBGLU2 6 6 small
## 6 measurement noninoculated inoculated 0.277 StHSP70 6 6 small
## 7 measurement noninoculated inoculated 0.324 StPti5 6 6 moderate
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 4.19 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.362 StPR1B 6 6 small
## 3 measurement noninoculated inoculated -1.45 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 3.13 StCAB 4 6 large
## 5 measurement noninoculated inoculated -0.215 StBGLU2 6 6 small
## 6 measurement noninoculated inoculated 0.556 StHSP70 6 6 moderate
## 7 measurement noninoculated inoculated -0.209 StPti5 6 6 small
## 8 measurement noninoculated inoculated -4.28 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0002442599 0.0004885198 *
## 3 StCPI8 noninoculated inoculated 0.0002442599 0.0004885198 *
## 4 StCAB noninoculated inoculated 0.0002442599 0.0004885198 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.0004885198 *
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 11 StRBOHD 0.324 0.332 0.330
## 2 12 StPR1B 0.000407 0.00000455 0.000173
## 3 10 StCPI8 0.164 0.249 0.337
## 4 9 StCAB 0.0306 0.0390 0.171
## 5 12 StBGLU2 0.00109 0.000552 0.00929
## 6 12 StHSP70 0.155 0.126 0.271
## 7 12 StPti5 0.00284 0.000925 0.00929
## 8 12 St13-LOX 0.0312 0.0474 0.0632
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 9 4.57 0.0613
## 2 StPR1B 1 10 29.5 0.000290
## 3 StCPI8 1 8 0.779 0.403
## 4 StCAB 1 7 0.148 0.712
## 5 StBGLU2 1 10 4.74 0.0546
## 6 StHSP70 1 10 0.396 0.543
## 7 StPti5 1 10 15.7 0.00268
## 8 St13-LOX 1 10 5.68 0.0384
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 9 4.40 0.0653
## 2 StPR1B 1 10 4.59 0.0579
## 3 StCPI8 1 8 0.0906 0.771
## 4 StCAB 1 7 0.231 0.645
## 5 StBGLU2 1 10 3.81 0.0795
## 6 StHSP70 1 10 0.0184 0.895
## 7 StPti5 1 10 10.7 0.00851
## 8 St13-LOX 1 10 4.40 0.0623
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 3.21 0.0734 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 7.61 0.00579 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 0.0695 0.792 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 0.466 0.495 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 7.59 0.00588 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 0.0916 0.762 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 7.59 0.00588 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 7.57 0.00592 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.661 StRBOHD 6 5 large
## 2 measurement noninoculated inoculated 0.603 StPR1B 6 6 large
## 3 measurement noninoculated inoculated 0.495 StCPI8 5 5 moderate
## 4 measurement noninoculated inoculated 0.245 StCAB 5 4 small
## 5 measurement noninoculated inoculated 0.601 StBGLU2 6 6 large
## 6 measurement noninoculated inoculated 0.324 StHSP70 6 6 moderate
## 7 measurement noninoculated inoculated 0.832 StPti5 6 6 large
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.70 StRBOHD 6 5 large
## 2 measurement noninoculated inoculated -1.08 StPR1B 6 6 large
## 3 measurement noninoculated inoculated -1.37 StCPI8 5 5 large
## 4 measurement noninoculated inoculated -0.254 StCAB 5 4 small
## 5 measurement noninoculated inoculated -1.09 StBGLU2 6 6 large
## 6 measurement noninoculated inoculated -0.712 StHSP70 6 6 moderate
## 7 measurement noninoculated inoculated -1.60 StPti5 6 6 large
## 8 measurement noninoculated inoculated -2.41 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0236932096 0.037909135 *
## 2 StPR1B noninoculated inoculated 0.0183194919 0.037909135 *
## 5 StBGLU2 noninoculated inoculated 0.0195407914 0.037909135 *
## 7 StPti5 noninoculated inoculated 0.0002442599 0.001954079 *
## 8 St13-LOX noninoculated inoculated 0.0019540791 0.007816317 *
results_PS68 = run_analysis_for_strain(data = Rywal
, strain = "PS-68"
, myorder
, pal
, groupvars
, measurevar
, my_dir
, plot_gene_expression
, test_and_plot)
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.538 0.519 0.424
## 2 12 StPR1B 0.00485 0.00198 0.0133
## 3 12 StCPI8 0.00485 0.00198 0.00771
## 4 10 StCAB 0.0381 0.0405 0.173
## 5 12 StBGLU2 0.0135 0.0307 0.129
## 6 12 StHSP70 0.538 0.519 0.250
## 7 11 StPti5 0.0225 0.0307 0.132
## 8 12 St13-LOX 0.116 0.111 0.173
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 0.0204 0.889
## 2 StPR1B 1 10 14.2 0.00364
## 3 StCPI8 1 10 27.2 0.000393
## 4 StCAB 1 8 4.46 0.0676
## 5 StBGLU2 1 10 2.46 0.148
## 6 StHSP70 1 10 10.6 0.00863
## 7 StPti5 1 9 0.104 0.755
## 8 St13-LOX 1 10 3.34 0.0974
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 0.0150 0.905
## 2 StPR1B 1 10 2.64 0.135
## 3 StCPI8 1 10 24.8 0.000552
## 4 StCAB 1 8 4.02 0.0800
## 5 StBGLU2 1 10 1.62 0.232
## 6 StHSP70 1 10 10.1 0.00984
## 7 StPti5 1 9 0.0408 0.844
## 8 St13-LOX 1 10 2.74 0.129
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 0.0720 0.788 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 2.86 0.0909 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 7.57 0.00592 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 4.52 0.0336 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 1.79 0.180 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 6.09 0.0136 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 0.355 0.551 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 4.16 0.0415 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.786 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.277 StPR1B 6 6 small
## 3 measurement noninoculated inoculated 0.693 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 0.809 StCAB 4 6 large
## 5 measurement noninoculated inoculated 0.370 StBGLU2 6 6 moderate
## 6 measurement noninoculated inoculated 0.231 StHSP70 6 6 small
## 7 measurement noninoculated inoculated 0.385 StPti5 6 5 moderate
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 2.81 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated -0.862 StPR1B 6 6 large
## 3 measurement noninoculated inoculated -1.47 StCPI8 6 6 large
## 4 measurement noninoculated inoculated 3.32 StCAB 4 6 large
## 5 measurement noninoculated inoculated -0.722 StBGLU2 6 6 moderate
## 6 measurement noninoculated inoculated -0.489 StHSP70 6 6 small
## 7 measurement noninoculated inoculated -0.497 StPti5 6 5 small
## 8 measurement noninoculated inoculated -3.64 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 StRBOHD noninoculated inoculated 0.0014655594 0.005862237 *
## 3 StCPI8 noninoculated inoculated 0.0092818759 0.018563752 *
## 4 StCAB noninoculated inoculated 0.0043966781 0.011724475 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.001954079 *
## #### ####
## Distribution tests
## #### ####
## # A tibble: 8 × 7
## n transcript Shapiro_Wilk_BH Anderson_Darling_BH Lilliefors_KS_BH
## <int> <fct> <dbl> <dbl> <dbl>
## 1 12 StRBOHD 0.605 0.741 0.878
## 2 12 StPR1B 0.000364 0.0000123 0.00000862
## 3 11 StCPI8 0.0915 0.166 0.432
## 4 9 StCAB 0.199 0.240 0.244
## 5 12 StBGLU2 0.00166 0.000675 0.000800
## 6 12 StHSP70 0.0435 0.0793 0.244
## 7 12 StPti5 0.0435 0.0424 0.0148
## 8 12 St13-LOX 0.0184 0.0105 0.0148
## # ℹ 2 more variables: Jarque_Bera_BH <dbl>, DAgostino_Skewness_BH <dbl>
## #### ####
## Quantile-Quantile plots
## #### ####
## #### ####
## Test for homogeneity of variance across groups
## #### ####
## #### ####
## Levene
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 1.95 0.193
## 2 StPR1B 1 10 7.75 0.0193
## 3 StCPI8 1 9 2.79 0.129
## 4 StCAB 1 7 0.758 0.413
## 5 StBGLU2 1 10 7.26 0.0225
## 6 StHSP70 1 10 0.343 0.571
## 7 StPti5 1 10 10.6 0.00866
## 8 St13-LOX 1 10 16.3 0.00236
## #### ####
## Brown-Forsythe
## #### ####
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 StRBOHD 1 10 1.93 0.195
## 2 StPR1B 1 10 5.86 0.0360
## 3 StCPI8 1 9 2.64 0.139
## 4 StCAB 1 7 0.247 0.634
## 5 StBGLU2 1 10 4.06 0.0715
## 6 StHSP70 1 10 0.118 0.738
## 7 StPti5 1 10 6.73 0.0268
## 8 St13-LOX 1 10 14.5 0.00347
## #### ####
## Fligner
## #### ####
## # A tibble: 8 × 5
## # Groups: transcript [8]
## transcript statistic p.value parameter method
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 StRBOHD 1.63 0.202 1 Fligner-Killeen test of homogeneity of…
## 2 StPR1B 7.61 0.00579 1 Fligner-Killeen test of homogeneity of…
## 3 StCPI8 1.38 0.241 1 Fligner-Killeen test of homogeneity of…
## 4 StCAB 0.0363 0.849 1 Fligner-Killeen test of homogeneity of…
## 5 StBGLU2 7.57 0.00592 1 Fligner-Killeen test of homogeneity of…
## 6 StHSP70 0.0202 0.887 1 Fligner-Killeen test of homogeneity of…
## 7 StPti5 7.59 0.00588 1 Fligner-Killeen test of homogeneity of…
## 8 St13-LOX 6.09 0.0136 1 Fligner-Killeen test of homogeneity of…
## #### ####
## Wilcoxon effect size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.508 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated 0.488 StPR1B 6 6 moderate
## 3 measurement noninoculated inoculated 0.661 StCPI8 5 6 large
## 4 measurement noninoculated inoculated 0.327 StCAB 5 4 moderate
## 5 measurement noninoculated inoculated 0.370 StBGLU2 6 6 moderate
## 6 measurement noninoculated inoculated 0.139 StHSP70 6 6 small
## 7 measurement noninoculated inoculated 0.832 StPti5 6 6 large
## 8 measurement noninoculated inoculated 0.832 St13-LOX 6 6 large
## #### ####
## Cohen's d Measure of Effect Size
## #### ####
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.08 StRBOHD 6 6 large
## 2 measurement noninoculated inoculated -1.01 StPR1B 6 6 large
## 3 measurement noninoculated inoculated -1.68 StCPI8 5 6 large
## 4 measurement noninoculated inoculated -0.225 StCAB 5 4 small
## 5 measurement noninoculated inoculated -0.952 StBGLU2 6 6 large
## 6 measurement noninoculated inoculated -0.340 StHSP70 6 6 small
## 7 measurement noninoculated inoculated -3.44 StPti5 6 6 large
## 8 measurement noninoculated inoculated -9.33 St13-LOX 6 6 large
## transcript group1 group2 p p.adj p.adj.signif
## 7 StPti5 noninoculated inoculated 0.0002442599 0.0009770396 *
## 8 St13-LOX noninoculated inoculated 0.0002442599 0.0009770396 *
6 sessionInfo
## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/Ljubljana
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_4.0.0 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 Rdpack_2.6.4 sandwich_3.1-1
## [4] MKinfer_1.2 rlang_1.1.6 multcomp_1.4-28
## [7] miceadds_3.18-36 tseries_0.10-58 matrixStats_1.5.0
## [10] compiler_4.4.1 vctrs_0.6.5 quadprog_1.5-8
## [13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
## [16] fastmap_1.2.0 backports_1.5.0 labeling_0.4.3
## [19] utf8_1.2.6 rmarkdown_2.30 exactRankTests_0.8-35
## [22] nloptr_2.2.1 purrr_1.0.4 xfun_0.53
## [25] glmnet_4.1-10 modeltools_0.2-24 jomo_2.7-6
## [28] cachem_1.1.0 jsonlite_2.0.0 gmp_0.7-5
## [31] pan_1.9 broom_1.0.10 parallel_4.4.1
## [34] R6_2.6.1 coin_1.4-3 bslib_0.9.0
## [37] stringi_1.8.7 RColorBrewer_1.1-3 rpart_4.1.24
## [40] car_3.1-3 boot_1.3-31 jquerylib_0.1.4
## [43] Rcpp_1.0.14 iterators_1.0.14 knitr_1.50
## [46] zoo_1.8-14 nnet_7.3-20 Matrix_1.7-1
## [49] splines_4.4.1 tidyselect_1.2.1 rstudioapi_0.17.1
## [52] dichromat_2.0-0.1 abind_1.4-8 yaml_2.3.10
## [55] codetools_0.2-20 curl_6.2.2 arrangements_1.1.9
## [58] lattice_0.22-6 tibble_3.3.0 quantmod_0.4.28
## [61] withr_3.0.2 S7_0.2.0 evaluate_1.0.5
## [64] moments_0.14.1 survival_3.8-3 zip_2.3.3
## [67] xts_0.14.1 pillar_1.11.1 ggpubr_0.6.1
## [70] carData_3.0-5 mice_3.18.0 nortest_1.0-4
## [73] foreach_1.5.2 stats4_4.4.1 reformulas_0.4.1
## [76] generics_0.1.4 TTR_0.24.4 scales_1.4.0
## [79] minqa_1.2.8 glue_1.8.0 tools_4.4.1
## [82] data.table_1.17.8 MKdescr_0.9 lme4_1.1-37
## [85] openxlsx_4.2.8 ggsignif_0.6.4 mvtnorm_1.3-3
## [88] cowplot_1.2.0 grid_4.4.1 mitools_2.4
## [91] tidyr_1.3.1 rbibutils_2.3 libcoin_1.0-10
## [94] nlme_3.1-166 Formula_1.2-5 cli_3.6.5
## [97] dplyr_1.1.4 ggh4x_0.3.1 gtable_0.3.6
## [100] rstatix_0.7.2 sass_0.4.10 digest_0.6.37
## [103] TH.data_1.1-4 farver_2.1.2 htmltools_0.5.8.1
## [106] lifecycle_1.0.4 mitml_0.4-5 MASS_7.3-64